The Synchronization of Data Collection for Real-time Group Recognition

Abstract

It is commonplace for people to perform various kinds of activities in groups. The recognition of groups is of importance in many applications including crowd evacuation, teamwork coordination, and advertising for groups. Existing group recognition approaches require snapshots of human trajectories, which is often impossible in real life situation due to the differences of data collection start time and frequency. This paper proposes an approach to synchronize the trajectory data of people by interpolation based on Catmull-Rom Spline. The optimal interpolating points are computed based on our proposed error function. Moreover, we propose an approach to assign the groups proper colors and then uses the hot map to show the dynamic changes of groups graphically. A real-life data set is used to validate the effectiveness of the proposed approach. The results show that 97.9% accuracy of group recognition can be achieved and the dynamic changes of groups is well shown.